The 3rd Generation Partnership Project (3GPP) is currently working on next generation telecommunications systems, which in the 3GPP terminology are called Long Term Evolution (LTE) systems. An important feature of LTE systems will be a high peak data rate of 100 Mbps and beyond. The high peak data rate is achieved by implementing, among other techniques, link adaptation and Hybrid Automatic Retransmission Request (Hybrid ARQ) schemes.
In short, link adaptation allows a base station to select modulation and coding parameters individually per user terminal based on the current channel quality. Hybrid ARQ schemes, on the other hand, enhance the acknowledgement, retransmission and time-out features of conventional ARQ schemes with forward error correction coding (using, for example, Turbo Codes) and with the transmission of error detection information (such as Cyclic Redundancy Check bits).
Hybrid ARQ schemes improve system throughput by combining (rather than discarding) information received via previous erroneous transmission attempts with information received with a current attempt. For this reason, Hybrid ARQ schemes require memory resources for temporarily storing the information received via the erroneous transmission attempts. The information that needs to be stored includes the received data bits as well as related reliability information (the so-called soft bits).
A soft bit is produced by a decoder front end for each data bit in the received signal and can be regarded as a measure of how likely it is that the data bit is a 0 or a 1. Accordingly, while a conventional decoder front end would simply decide if an internal analog voltage level is above or below a given threshold voltage level to identify the received analog information as either 0 or 1, the front end of, for example, a Turbo Code decoder would provide an integer measure (the soft bit) of how far the internal analog voltage is from the threshold voltage level.
The conventional “hard” decision and the improved “soft” decision techniques are illustrated in FIGS. 1A (PRIOR ART) and 1B (PRIOR ART), respectively. FIG. 1A and FIG. 1B both show the probability density p as a function of an analog voltage level x(T)=mi+n0, with mi being representative of the signal component and n0 being representative of the noise component in the received signal. The voltage level x(T) will have the mean value m1 in case a 1 is transmitted, and the mean value m2 in case a 0is transmitted.
In the “hard” decision scenario shown in FIG. 1A, the level of x(T) is simply compared with a given threshold voltage level to decide if 1 or 0 has been transmitted. In the exemplary “soft” decision scenario shown in FIG. 1B, on the other hand, eight soft bit quantizing ranges (corresponding to a resolution of 3 bits) are defined indicating how far the level of x(T) is from the threshold value level. The word 111, for example, would indicate a decision for 1 with high reliability, while the word 100 would indicate a decision for 1 with low reliability. This additional reliability information generated in the “soft” decision scenario results in a reduced bit error rate compared to the “hard” decision scenario, or in a lower required Signal-to-Noise Ratio (SNR) for achieving the same bit error rate.
Increasing the resolution of soft bit quantization (up to a certain extent) helps to lower the bit error rate. However, an increased resolution also leads to increased memory requirements for Hybrid ARQ buffers because the soft bits have to be temporarily stored as outlined above. In particular in LTE and similar systems, the memory requirements for Hybrid ARQ schemes may thus get very demanding due to the inherently high peak data rates. Thus, smart methods have been proposed for reducing the resolution while maintaining the coding performance at a high level.
Such optimization methods as described for example in G. Jeong and D. Hsia, “Optimal Quantization for Soft-Decision Turbo Decoder”, (VTC Fall '99), Amsterdam, The Netherlands, September 1999, typically require knowledge of the effective code rate and of the utilized modulation scheme. However, combined link adaptation and Hybrid ARQ-induced retransmission make it rather difficult to predict the effective code rate. This difficulty is mainly caused by the fact that at the time the “soft” decision has to be taken, the code rate selected by the link adaptation mechanism may not (yet) be known to the decision mechanism, and the decision mechanism also lacks knowledge whether or not (further) retransmissions are needed.
A further problem results from the fact that reliability information may get effectively lost depending on the current SNR as will now be explained in more detail. The mean value of the unquantized soft bit magnitude as well as its variance are basically proportional to the current SNR. This is illustrated for the example of Quadrature Phase Shift Keying (QPSK) and the SNR values of 0 dB and 6 dB in the soft bit histograms of FIGS. 2A (PRIOR ART) and 2B (PRIOR ART), respectively.
For a SNR value of 0 dB (FIG. 2A), the mean value is that small that the curves from both sides heavily overlap around the centre. As the magnitudes of the unquantized soft bit magnitudes are thus close to zero, the quantized soft bits will in most cases either be 0 or 1. For a SNR value of 6 dB (FIG. 2B), on the other hand, the magnitudes of the unquantized soft bits are mostly outside the allowed integer range, which results after clipping and quantization in soft bits assuming the maximum allowed integer values. In both cases, the available soft bit resolution is not utilized efficiently.
To compensate for the influence of the SNR on the soft bit quantization process, it is proposed in WO 2007/092744 A2 to scale the (analog) soft bit magnitude prior to the quantization step. The scaling step helps to ensure that the quantized soft bits make better use of the available integer value range. A scaling factor applied during the scaling is generally selected to be inversely proportional to a measured SNR value.
In fading channel environments, channel gain and hence the SNR is varying quickly. This means that the SNR varies even during one single code word. In LTE systems, which rely on Orthogonal Frequency Division Multiplexing (OFDM), the SNR may thus vary during one code word. In such situations it might be considered to derive the scaling factor based on a mean SNR value averaged over one code word. It has, however, been found that a simple averaging generally raises the problem that either soft bits corresponding to low SNR values or soft bits corresponding to high SNR values will have a sub-optimal precision (similar to the scenarios illustrated in FIGS. 2A and 2B).